Machine learning and visualization in Python for Earth science
Python offers a wide variety of open-source libraries covering a huge range of functionality, but it can be difficult to work out which libraries are suitable for which tasks. The EarthML project helps to:
Demonstrate how to use Python tools for machine learning and analysis in the earth sciences
Identify libraries suitable for working with earth-science data
Make improvements to these libraries as needed to help improve earth-science workflows
EarthML contains no code of its own, only tutorials and examples showing how to use packages like:
hvPlot: Simple data-centric API for plotting, building on:
Panel: Dashboards, apps, and widgets for any library’s plots.
ML tools: (representative only – use any you like!)
The EarthML Tutorial offers a general-purpose overview of the concepts and tools involved, and the Topics section shows examples of how these tools may be used to perform machine learning and related tasks in the Earth sciences, such as:
Heat and Trees
Running the EarthML projects¶
The EarthML topic examples are included among the many examples available on examples.pyviz.org. Each project is encapsulated in a reproducible project that includes an environment specification, an optional intake data catalog as well as notebooks and any other necessary assets.